code: def corresponding_points_alignment(
      batch_size=10,
      n_points=100,
      dim=3,
      use_pointclouds=False,
      estimate_scale=False,
      allow_reflection=False,
      reflect=False,
      random_weights=False,
  ):
      device = torch.device("cuda:0")
  
      # initialize a ground truth point cloud
      X = TestCorrespondingPointsAlignment.init_point_cloud(
          batch_size=batch_size,
          n_points=n_points,
          dim=dim,
          device=device,
          use_pointclouds=use_pointclouds,
          random_pcl_size=True,
      )
  
      # generate the true transformation
      (
          R,
          T,
          s,
      ) = TestCorrespondingPointsAlignment.generate_pcl_transformation(
          batch_size=batch_size,
          scale=estimate_scale,
          reflect=reflect,
          dim=dim,
          device=device,
      )
  
      # apply the generated transformation to the generated
      # point cloud X
      X_t = _apply_pcl_transformation(X, R, T, s=s)
  
      weights = None
      if random_weights:
          template = X.points_padded() if use_pointclouds else X
          weights = torch.rand_like(template[:, :, 0])
          weights = weights / weights.sum(dim=1, keepdim=True)
          # zero out some weights as zero weights are a common use case
          # this guarantees there are no zero weight
          weights *= (weights * template.size()[1] > 0.3).to(weights)
          if use_pointclouds:  # convert to List[Tensor]
              weights = [
                  w[:npts]
                  for w, npts in zip(weights, X.num_points_per_cloud())
              ]
  
      torch.cuda.synchronize()
  
      def run_corresponding_points_alignment():
          points_alignment.corresponding_points_alignment(
              X,
              X_t,
              weights,
              allow_reflection=allow_reflection,
              estimate_scale=estimate_scale,
          )
          torch.cuda.synchronize()
  
      return run_corresponding_points_alignment
  
imports: import unittest
  import numpy
  import torch
  from pytorch3d.ops import points_alignment
  from pytorch3d.structures.pointclouds import Pointclouds
  from pytorch3d.transforms import rotation_conversions
  from .common_testing import get_tests_dir, TestCaseMixin
label_desc: Use image node_id 6 for calling the TestCorrespondingPointsAlignment obj's underlying member method code with example usage: obj.corresponding_points_alignment(batch_size, n_points, dim, use_pointclouds, estimate_scale, allow_reflection, reflect, random_weights) and returns: run_corresponding_points_alignment